Emily Chen
Software Development Engineer Intern at Amazon
Washington DC-Baltimore Area United States
Summary
🤩
Rockstar🎓
Top SchoolEmily Chen is a software engineer and UI/UX-minded problem solver from the Bay Area, pursuing Computer Science at UIUC with a 3.98 GPA and six years of hands-on experience across internships and product roles. She builds full-stack features—from Flask APIs and React TypeScript extensions to AWS-deployed services—focused on user impact, inclusive language tooling, and accessible educational products. Emily contributed to scikit-learn by adding Array API support for several regression metrics and distance functions, demonstrating a capacity to improve widely used ML libraries. Comfortable with C++, Java, Python, React, and cloud tooling, she blends UX research and testing discipline (80%+ coverage on some projects) to ship reliable, user-centered systems. She seeks roles that pose hard technical challenges while delivering measurable benefits for people.
6 years of coding experience
4 years of employment as a software developer
4.413, 4.413 at Thomas Jefferson High for Science And TechnologyBachelor of Science - BS, Computer Science and Advertising, 3.98, Bachelor of Science - BS, Computer Science and Advertising, 3.98 at University of Illinois Urbana-Champaign
English, Chinese
Github Skills (10)
scikit-learn10
machine-learning10
api10
python10
data-science10
scikit10
pytest9
numpy9
statistics8
data-analysis8
Programming languages (6)
Github contributions (5)
scikit-learn/scikit-learn
Jun 2024 - Mar 2025
scikit-learn: machine learning in Python
Role in this project:
ML Engineer / Data Scientist
ML Engineer / Data ScientistContributions:28 reviews, 13 PRs, 56 comments in 8 months
Contributions summary:Emily primarily contributed to adding and supporting array API functionality within the scikit-learn library, specifically focusing on regression metrics. Their work included implementing array API support for `d2_tweedie_score`, `mean_gamma_deviance`, `mean_absolute_percentage_error`, and `mean_poisson_deviance`, along with other related metrics, and the `cosine_distances` function, ensuring broader compatibility with different array implementations. These changes involved modifying existing functions, updating documentation, and adding new test cases to accommodate array API usage. This work reflects a focus on enhancing the library's capabilities and compatibility for diverse machine-learning workflows.
data-analysispythonstatisticsdata-sciencelearn-machine-learning
EmilyXinyi/scikit-learn
Jun 2024 - Mar 2025
scikit-learn: machine learning in Python
Contributions:111 pushes, 18 branches in 8 months
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